Can machine learning on learner analytics produce a predictive model on student performance?

Research output: Contribution to conferencePaper

Abstract

The aim of this research is to analysis past student learner analytics using machine learning algorithms that had undertaken a web development and programming module. By specifically using the access and error web server logs from each student web server it provides a deeper learner analytic data. The web server logs every web file access and error access from a browser so in turn each data file can directly relate to a student's engagement level and assessment strategy. Each log holds several types of information which is filtered to make sure only the relevant dataset is processed through the machine learning framework. The students' performance data was also gathered so that a data mining analysis the learner analytics could be performed to see if there is any correlation between log data and their final assessment mark.

WEKA, an open source machine learning software suite was used to perform data mining algorithms on the large data set. Applying data mining in education is an emerging research field also known as educational data mining (EDM) and some studies have found that EDM could predict with a success rate of more than 80% which students will or will not graduate. By using data mining on student's assignment development data it would show a correlation and therefore a predictive model could be produced.
LanguageEnglish
Publication statusPublished - 21 Jun 2017
EventInnovative and Creative Education and Technology International Conference - University of Extremadura, Badajoz, Spain
Duration: 21 Jun 201723 Jun 2017
http://www.icetic.net/

Conference

ConferenceInnovative and Creative Education and Technology International Conference
Abbreviated titleICETIC 2017
CountrySpain
CityBadajoz
Period21/06/201723/06/2017
Internet address

Fingerprint

Data mining
Learning systems
Students
Servers
Learning algorithms
Education

Cite this

Busch, J., Hanna, P., O'Neill, I., McGowan, A., & Collins, M. (2017). Can machine learning on learner analytics produce a predictive model on student performance?. Paper presented at Innovative and Creative Education and Technology International Conference , Badajoz, Spain.
Busch, John ; Hanna, Philip ; O'Neill, Ian ; McGowan, Aidan ; Collins, Matthew. / Can machine learning on learner analytics produce a predictive model on student performance?. Paper presented at Innovative and Creative Education and Technology International Conference , Badajoz, Spain.
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abstract = "The aim of this research is to analysis past student learner analytics using machine learning algorithms that had undertaken a web development and programming module. By specifically using the access and error web server logs from each student web server it provides a deeper learner analytic data. The web server logs every web file access and error access from a browser so in turn each data file can directly relate to a student's engagement level and assessment strategy. Each log holds several types of information which is filtered to make sure only the relevant dataset is processed through the machine learning framework. The students' performance data was also gathered so that a data mining analysis the learner analytics could be performed to see if there is any correlation between log data and their final assessment mark. WEKA, an open source machine learning software suite was used to perform data mining algorithms on the large data set. Applying data mining in education is an emerging research field also known as educational data mining (EDM) and some studies have found that EDM could predict with a success rate of more than 80{\%} which students will or will not graduate. By using data mining on student's assignment development data it would show a correlation and therefore a predictive model could be produced.",
author = "John Busch and Philip Hanna and Ian O'Neill and Aidan McGowan and Matthew Collins",
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month = "6",
day = "21",
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note = "Innovative and Creative Education and Technology International Conference , ICETIC 2017 ; Conference date: 21-06-2017 Through 23-06-2017",
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Busch, J, Hanna, P, O'Neill, I, McGowan, A & Collins, M 2017, 'Can machine learning on learner analytics produce a predictive model on student performance?' Paper presented at Innovative and Creative Education and Technology International Conference , Badajoz, Spain, 21/06/2017 - 23/06/2017, .

Can machine learning on learner analytics produce a predictive model on student performance? / Busch, John; Hanna, Philip; O'Neill, Ian; McGowan, Aidan; Collins, Matthew.

2017. Paper presented at Innovative and Creative Education and Technology International Conference , Badajoz, Spain.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Can machine learning on learner analytics produce a predictive model on student performance?

AU - Busch, John

AU - Hanna, Philip

AU - O'Neill, Ian

AU - McGowan, Aidan

AU - Collins, Matthew

PY - 2017/6/21

Y1 - 2017/6/21

N2 - The aim of this research is to analysis past student learner analytics using machine learning algorithms that had undertaken a web development and programming module. By specifically using the access and error web server logs from each student web server it provides a deeper learner analytic data. The web server logs every web file access and error access from a browser so in turn each data file can directly relate to a student's engagement level and assessment strategy. Each log holds several types of information which is filtered to make sure only the relevant dataset is processed through the machine learning framework. The students' performance data was also gathered so that a data mining analysis the learner analytics could be performed to see if there is any correlation between log data and their final assessment mark. WEKA, an open source machine learning software suite was used to perform data mining algorithms on the large data set. Applying data mining in education is an emerging research field also known as educational data mining (EDM) and some studies have found that EDM could predict with a success rate of more than 80% which students will or will not graduate. By using data mining on student's assignment development data it would show a correlation and therefore a predictive model could be produced.

AB - The aim of this research is to analysis past student learner analytics using machine learning algorithms that had undertaken a web development and programming module. By specifically using the access and error web server logs from each student web server it provides a deeper learner analytic data. The web server logs every web file access and error access from a browser so in turn each data file can directly relate to a student's engagement level and assessment strategy. Each log holds several types of information which is filtered to make sure only the relevant dataset is processed through the machine learning framework. The students' performance data was also gathered so that a data mining analysis the learner analytics could be performed to see if there is any correlation between log data and their final assessment mark. WEKA, an open source machine learning software suite was used to perform data mining algorithms on the large data set. Applying data mining in education is an emerging research field also known as educational data mining (EDM) and some studies have found that EDM could predict with a success rate of more than 80% which students will or will not graduate. By using data mining on student's assignment development data it would show a correlation and therefore a predictive model could be produced.

M3 - Paper

ER -

Busch J, Hanna P, O'Neill I, McGowan A, Collins M. Can machine learning on learner analytics produce a predictive model on student performance?. 2017. Paper presented at Innovative and Creative Education and Technology International Conference , Badajoz, Spain.